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DLDTI: a learning-based framework for drug-target interaction identification using neural networks and network

Yihan Zhao1, Kai Zheng2, Baoyi Guan3

  • 1Department of Graduate School, Beijing University of Chinese Medicine, Beijing, China.

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|November 14, 2020
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Summary

This study introduces DLDTI, a computational model for predicting drug-target interactions (DTIs). DLDTI successfully identified tetramethylpyrazine as a potential treatment for atherosclerosis by targeting platelet signaling pathways.

Keywords:
AtherosclerosisDeep convolutional neural networksDrug-target interactionHeterogeneous informationNetwork representation learningStacked auto-encoder

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Area of Science:

  • Computational biology
  • Pharmacology
  • Bioinformatics

Background:

  • Drug repositioning accelerates drug development by identifying new uses for existing drugs.
  • Computational methods are crucial for predicting drug-target interactions (DTIs) due to the high cost and time of experimental validation.
  • Elucidating novel molecular mechanisms of known drugs is essential for drug discovery.

Purpose of the Study:

  • To develop a novel computational model for predicting drug-target interactions (DTIs).
  • To identify potential therapeutic targets for existing drugs using network representation learning and convolutional neural networks.
  • To experimentally validate the predicted drug-target interactions for tetramethylpyrazine (TMPZ) in atherosclerosis.

Main Methods:

  • A novel DTI prediction model, DLDTI, was developed using network representation learning and convolutional neural networks.
  • The model integrates complex network topology with diverse heterogeneous data, learning low-dimensional features from noisy, high-dimensional biological data.
  • Candidate DTIs were ranked based on proximity in the learned optimal mapping space.

Main Results:

  • The DLDTI model demonstrated high performance with an AUC of 0.9172 in cross-validation studies.
  • Experimental validation confirmed that tetramethylpyrazine (TMPZ) attenuates atherosclerosis by inhibiting platelet signaling pathways.
  • Key pathways identified include PI3K/Akt, cAMP, and calcium signaling, with 190 of 288 predicted targets involved in platelet activation.

Conclusions:

  • The DLDTI model is a valuable tool for predicting promising drug-target interaction candidates for experimental validation.
  • Tetramethylpyrazine (TMPZ) shows therapeutic potential for atherosclerosis by modulating platelet signal transduction.
  • The study provides a computational framework and validated findings for drug repositioning and mechanistic studies.